Pith. sign in

REVIEW 4 major objections 6 minor 125 references

WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch

T0 review · 4 major / 6 minor · reviewed 2026-08-08 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A smartwatch can learn a user's own undesirable micro-actions from a few examples and reduce them by 64%.

desk verdict A genuinely useful few-shot customization pipeline for user-defined micro-actions, but the headline intervention result rests on single-coder video annotation that is not shown to be reliable or blind to condition. read the letter →

arxiv 2502.05783 v1 pith:4EMXBM6L submitted 2025-02-09 cs.HC cs.AIcs.LG

Ying Lei , Yancheng Cao , Will Wang , Yuanzhe Dong , Changchang Yin , Weidan Cao , Ping Zhang , Jingzhen Yang
show 8 more authors
Bingsheng Yao Yifan Peng Chunhua Weng Randy Auerbach Lena Mamykina Dakuo Wang Yuntao Wang Xuhai Xu
This is my paper · ORCID
classification cs.HCcs.AIcs.LG
keywords few-shotlearningjust-in-timeinterventionpersonalizedbehaviorchangesmartwatchsensingIMUactivityrecognitionbody-focusedrepetitivebehaviorswearable
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

WatchGuardian is a smartwatch system that lets each user specify their own undesirable micro-action—nail-biting, lip-picking, leg-shaking, or a self-named behavior—and then receive a vibration reminder the moment the watch recognizes it. The paper's central claim is that a few-shot customization pipeline can learn such an action from one to ten example recordings: action-level accuracy rises from 76.8% with one shot to 84.7% with five and 87.7% with ten. In a controlled two-session study, the system reduced the time spent on the target action by $64.0 \pm 22.6\%$ relative to a pre-intervention phase, and outperformed a rule-based system that notified every ten minutes by 29.0 percentage points. If these results hold, personalized just-in-time intervention stops being limited to behaviors common enough to pre-train and becomes something an individual can define in under a minute.

What carries the argument

The load-bearing object is the three-stage few-shot customization pipeline: (1) a self-supervised pre-trained encoder for wrist-worn accelerometer signals; (2) supervised finetuning on fine-grained hand-activity data plus collected negative examples to sharpen the encoder on small, similar movements; and (3) a per-user classification head trained on a user's few recordings after a data augmentation and synthesis stage that multiplies the sample size by about 143. The third stage is what carries the personalization claim: it converts one to ten recordings of a self-named action into a stable binary or multi-class detector without requiring the user to collect a large dataset.

What would settle it

Have two independent coders annotate the same ceiling-video recordings and compute inter-rater reliability; if agreement is poor, the reported reduction is not established. Alternatively, rerun the intervention study with a follow-up measurement 24 hours later; if the relative duration of target actions returns to its pre-intervention level, the effect is momentary rather than lasting behavior change.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that user-defined, idiosyncratic actions can be brought into a just-in-time intervention loop with very little labeled data. The pipeline starts from a self-supervised model pre-trained on large-scale wrist accelerometer data, finetunes it on fine-grained hand-activity datasets, and then, for each new user and action, expands a handful of example recordings through six augmentation techniques and a segment-splicing synthesis step (about 143x data growth) before training a light classification head. Evaluated offline on 26 participants and 17 actions (five predetermined plus twelve self-defined), the action-level F1 score reached 74.8% with one shot, 84.2% with five, and 87.2% with ten. In the multi-hour intervention study with 21 participants, the just-in-time reminders reduced target-action duration to $36.0 \pm 22.6\%$ of its pre-intervention level, versus $65.0 \pm 47.5\%$ for the rule-based baseline, a 29.0% advantage that survived controlling for notification count. The authors also report that the effect persisted into a short 10-minute post-intervention window and that users sometimes perceived the AI's vibration as stronger than the identical baseline vibration.

Load-bearing premise

The measured reduction in undesirable actions rests on a single coder's manual annotation of ceiling-camera video being an unbiased and accurate record of when those actions occurred; there is no second coder to check reliability, and the post-intervention window is only 10 minutes.

Editorial extensions

If this is right

  • A user needs only one to ten examples to teach the watch a new personal action; with five examples the action-level accuracy is 84.7% and F1 84.2%.
  • Delivering reminders exactly when the action is detected reduces target-action duration by about two-thirds relative to no intervention, about 29 percentage points more than a fixed ten-minute reminder schedule.
  • The advantage is largest in engaging tasks: when users are absorbed in what they are watching, just-in-time timing matters much more than when they are bored and easily interruptible.
  • A short post-intervention window shows the reduction persists at least briefly after reminders stop, indicating the effect is not purely momentary.
  • Users' subjective experience of an AI-timed intervention can diverge from its objective properties, with the identical vibration feeling stronger when delivered just-in-time.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, the same pipeline could move to other sensor placements (rings, headbands, added gyroscope or physiological channels) to catch actions a single wrist accelerometer cannot separate; the paper gestures at this but tests only the watch.
  • Beyond the paper, the natural next test is a clinical population with body-focused repetitive behaviors, where symptom-specific outcomes and longer follow-up could determine whether a 64% reduction in a two-day lab study translates into weeks-long habit change.
  • Beyond the paper, the reported distortion of perceived vibration strength suggests that perceived intervention intensity is a design lever independent of physical intensity: a system can feel stronger by being more precisely timed, not by vibrating harder.
  • Beyond the paper, replacing single-coder video annotation with automated pose-based annotation or a second sensor modality would make the 64% figure reproducible and checkable in larger field studies.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper presents WatchGuardian, a smartwatch-based just-in-time intervention system that lets users define their own undesirable micro-actions and train a personalized detector from a small number of samples. The technical pipeline combines a self-supervised pretrained IMU model, supervised finetuning on public hand-gesture datasets, and a data augmentation/synthesis step for few-shot customization. The offline evaluation with 26 participants reports action-level accuracy of 76.8%, 84.7%, and 87.7% with one, five, and ten shots, and the paper claims robustness when adding multiple actions. The intervention study with 21 participants compares WatchGuardian with a rule-based reminder system and reports a 64.0±22.6% reduction in the target action and a 29.0% advantage over the baseline, together with qualitative findings on user perceptions of the AI intervention.

Significance. If the reported intervention effect is valid, WatchGuardian is a useful step toward personalized, user-defined JITI for idiosyncratic micro-actions, an area that is largely underserved by prior work. The offline evaluation is a notable strength: 49,140 trained models, held-out rounds for training/validation/test, and use of a public pretrained SSL model and finetuning datasets give the few-shot accuracy claims a credible empirical basis, and the authors point to open resources for reproducibility. The qualitative findings on distorted perception of intervention strength and on varied human-AI collaborative relationships are also valuable for future design. However, the central behavioral claim rests on manual video annotation that is not checked for reliability or blinding, and the exclusion of participants is not justified with criteria; these issues must be resolved before the headline result can be considered established.

major comments (4)
  1. [Sec. 5.3 / 5.4.1] The headline result, a 64.0±22.6% reduction in undesirable actions and a 29.0% advantage over the rule-based baseline, is measured from manual annotation of ceiling-camera video, but the paper reports only that the authors 'manually annotated the video' and gives no inter-rater reliability, no second coder, and no statement that the annotator was blind to the intervention condition. Because the two conditions are visually distinguishable (a reminder every 10 minutes in the baseline versus just-in-time notifications with a 5-minute cool-down in WatchGuardian), an unblinded annotator could count ambiguous micro-movements systematically differently across conditions. Please provide the annotation protocol, at least two independent coders on a subset with agreement statistics, and evidence of blinding or a condition-blind re-annotation; without this, the central behavioral claim is not established.
  2. [Sec. 5.1] Five of 26 participants were removed as outliers for not following the study protocol, but no criteria or examples are given. The intervention comparison is then based on N=21, and the exclusion could bias the effect in either direction. Please specify the protocol violations, how they were determined and by whom, whether any exclusions occurred in each condition, and report a sensitivity analysis that includes all 26 participants.
  3. [Sec. 3.1.3 / Sec. 4.2] The smoothing threshold of 3 is described as selected by grid search, but the paper does not state which data split was used for the search. If the threshold was chosen using the same held-out test rounds that produce Table 1, the action-level accuracies and F1 scores are optimistically biased. Please describe the grid-search split and, if necessary, re-report the action-level numbers with the threshold selected on the validation set only.
  4. [Sec. 5.4.1] The generalized linear mixed model used to compare conditions is incompletely specified: there is no distribution/link function, no random-effects structure, and no description of how the proportional relative-duration outcome was modeled. The reported chi-square statistics and p-values therefore cannot be checked. Please report the full model specification (outcome distribution, link, fixed and random effects, software) and the corresponding effect estimate with confidence interval.
minor comments (6)
  1. [Abstract and Sec. 4.2.1 / Table 1] The abstract attributes the 76.8% accuracy and 74.8% F1 score to 'three' examples, but Table 1 shows these values for one shot; the three-shot values are 83.2% and 82.5%. Please correct the abstract or the table so the shot counts match.
  2. [Sec. 4.1.2] The action name 'Lip Tearing' appears in the action list and Figure 4, while the introduction and related work use 'lip-picking'; please standardize the action name.
  3. [Sec. 3.1.3] The statement that augmentation and synthesis 'enlarged' the data 'by about 143 times' is ambiguous: augmentation alone is said to increase the data by 2^6−1=63 times and synthesis by about 80 times, which reads as either additive (63+80) or multiplicative (64×80); please clarify the intended arithmetic.
  4. [Sec. 5.4.1 / Fig. 8(a)] The post-intervention stage is only 10 minutes, so the phrase 'promising signals of a potential lasting effect' should be tempered or explicitly framed as preliminary, given the authors' own acknowledgment of this limitation.
  5. [Sec. 5.3] The video annotation is described as recording duration 'every 10 minutes'; please specify the annotation unit and whether duration was measured continuously or in bins.
  6. [Sec. 5.4.4] The SUS comparison uses a Wilcoxon rank sum test on a within-subject design; a paired test such as Wilcoxon signed-rank is appropriate unless the analysis was actually between-group. Please correct the test or clarify the design.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: few-shot accuracy is measured on held-out test rounds and the intervention effect is an observed outcome, not a derived prediction.

full rationale

WatchGuardian is an empirical systems paper; its headline numbers are measurements, not derivations. The offline few-shot accuracy (76.8% with one shot, 84.7% with five shots, 87.7% with ten shots) is computed on test rounds that are explicitly held out from training and validation: 'we randomly selected two rounds of recordings as the training set (up to 10 shots), one round as the validation set (5 shots), and the remaining two rounds as the test set (10 shots).' Thus the reported action-level accuracy is not a fitted constant reproduced from training data. The smoothing threshold of 3 is described as 'based on grid search,' but the paper does not state that the threshold was selected on the test set, so there is no documented reduction of the reported accuracy to a grid-search fit. The intervention claim—'WatchGuardian resulted in 36.0 ± 22.6% of the duration compared to the pre-intervention stage (i.e., a reduction of 64.0%)'—is an observed outcome derived from video annotation and GLMM comparison, not a quantity obtained by plugging the model's parameters back into the evaluation. Self-citations (e.g., Xu et al. [121], Time2Stop [79], TypeOut [123], GLOBEM [122]) appear in related-work and discussion positioning and are not load-bearing for the paper's central results. The reader-flagged concerns about the grid-search split and possible participant overlap in negative data would be data-leakage or reporting issues if substantiated, but the paper text does not exhibit them as constructional identities, so they do not rise to circularity under the required evidentiary standard.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

The central claim rests on an empirical ML pipeline with several hand-set parameters and domain assumptions. No new physical entities are introduced. The most important assumptions are that SSL representations transfer to wrist micro-actions, that the public datasets plus a small negative set cover the target behavior space, and that the single-coder video annotation is unbiased.

free parameters (7)
  • Smoothing threshold = 3 consecutive positive windows
    Selected by grid search in Sec. 3.1.3; directly changes action-level accuracy and F1 and real-time detection sensitivity, with no explicit statement that the grid search was restricted to validation data.
  • Positive-window label threshold = 3 seconds of target action in a 5-second window
    Hand-set in Sec. 3.1.3; controls the positive versus negative balance in the few-shot training data and affects all reported offline metrics.
  • Data augmentation magnitudes = zooming 0.9 to 1.0, scaling N(1, 0.22), time warping N(1, 0.052), noise level 0.01
    Chosen in Sec. 3.1.3 from the augmentation survey [36]; not fitted to data, but the few-shot accuracy depends on these settings.
  • Synthesis segment length range = 3 to 4.9 seconds
    Hand-set in Sec. 3.1.3; determines the synthesized positive samples used to train the customization head.
  • Sliding window size and step = 5-second width, 0.1-second step
    Chosen in Sec. 3.1.2 and Sec. 3.1.3 to match SSL pretraining and real-time 10 Hz classification; all evaluation metrics depend on these values.
  • Intervention cool-down = At most one notification per 5 minutes
    Design choice in Sec. 3.2; caps intervention frequency and shapes the notification-matching restriction in the evaluation.
  • Forced notification window = 20 minutes
    Added in Sec. 5.3 to equalize notification counts; if no action is detected, a notification fires, contaminating the AI-based condition with rule-like reminders.
assumptions (6)
  • domain assumption The pre-trained SSL model from Yuan et al. [127] provides transferable representations for fine-grained action recognition after supervised fine-tuning.
    Stages 1 and 2 of Sec. 3.1 rely on this; there is no formal guarantee that representations learned from free-living UK Biobank activities transfer to wrist-worn micro-actions.
  • domain assumption The combined public hand-gesture datasets (Hu et al. [34], Bhattacharya et al. [8]) and their manually unified label mapping are valid training data for the target undesirable micro-actions.
    Sec. 3.1.2 unifies labels across datasets; label errors or domain mismatch would propagate into the customized head.
  • domain assumption The self-collected negative dataset from 10 participants is representative of the non-target behavior distribution for all users.
    Sec. 3.1.2 collects negative data from a small supervised group; if these activities do not cover real-world distractors, false positives increase.
  • domain assumption Wrist-worn tri-axial accelerometer data at 30 Hz provides sufficient signal to discriminate all 17 self-defined actions used in the evaluation.
    The sensor choice and sampling rate are described in Sec. 3.1 and Figure 4; some actions such as lip-biting may produce wrist motion too subtle to detect reliably.
  • domain assumption Manual video coding of target action duration by a single coder is an unbiased ground truth.
    Sec. 5.3 annotates ceiling camera video; no inter-rater reliability is reported, so annotation noise is unmeasured.
  • domain assumption The GLMM specification and relative-duration normalization account for between-person baseline differences.
    Sec. 5.4 normalizes by the 30-minute pre-intervention stage; short baselines may be unstable, and the full model structure is not reported.

how reviews work

0 comments
Cite this review

Pith. "Pith review of WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch." pith.science (2026). https://pith.science/paper/4EMXBM6L

@misc{pith2026250205783,
  author       = {Pith},
  title        = {Pith review of: WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4EMXBM6L}},
  note         = {Machine review of arXiv:2502.05783}
}
read the original abstract

While just-in-time interventions (JITIs) have effectively targeted common health behaviors, individuals often have unique needs to intervene in personal undesirable actions that can negatively affect physical, mental, and social well-being. We present WatchGuardian, a smartwatch-based JITI system that empowers users to define custom interventions for these personal actions with a small number of samples. For the model to detect new actions based on limited new data samples, we developed a few-shot learning pipeline that finetuned a pre-trained inertial measurement unit (IMU) model on public hand-gesture datasets. We then designed a data augmentation and synthesis process to train additional classification layers for customization. Our offline evaluation with 26 participants showed that with three, five, and ten examples, our approach achieved an average accuracy of 76.8%, 84.7%, and 87.7%, and an F1 score of 74.8%, 84.2%, and 87.2% We then conducted a four-hour intervention study to compare WatchGuardian against a rule-based intervention. Our results demonstrated that our system led to a significant reduction by 64.0 +- 22.6% in undesirable actions, substantially outperforming the baseline by 29.0%. Our findings underscore the effectiveness of a customizable, AI-driven JITI system for individuals in need of behavioral intervention in personal undesirable actions. We envision that our work can inspire broader applications of user-defined personalized intervention with advanced AI solutions.

Figures

Figures reproduced from arXiv: 2502.05783 by the authors.

Figure 1
Figure 1. WatchGuardian empowers users to easily define personal actions that they want to receive just-in-time intervention [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Three-stage Few-shot Pipeline for Model Customization. (A) Stage 1: We adopted A pre-trained SSL model for human [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Smartwatch Interface Designs. (a) Few-shot data collection interface, where a user can define the target behavior [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Target Actions for Evaluation. (1-5) presents the five pre-determined actions. (6-17) visualizes new target behaviors [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Few-shot Learning Pipeline Performance of Accuracy and F1 Score. We experimented with different numbers of shots [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Model Performance of Recognizing Each Action with 1, 5, or 10 Shots. For consistency, each action was added alone [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: WatchGuardian Intervention Evaluation Setup. (a) The sketch of the study room and apparatus setup for intervention. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: (a) Relative Duration of target action every 10 minutes in intervention and post-intervention stages (compared to the [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: (a) Relative duration of target action for participants who watched engaging videos. (b) Relative duration of target [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

125 extracted references · 53 canonical work pages

  1. [1]

    Ahmad Akl, Chen Feng, and Shahrokh Valaee. 2011. A Novel Accelerometer-Based Gesture Recognition System. IEEE Transactions on Signal Processing 59, 12 (Dec. 2011), 6197–6205. https://doi.org/10.1109/TSP.2011.2165707

    work page arXiv 2011

  2. [2]

    Ada Alevizaki and Niki Trigoni. 2022. watchHAR: A Smartwatch IMU dataset for Activities of Daily Living . https://doi.org/10.5281/ zenodo.7092553

    2022

  3. [3]

    Rawan Alharbi, Soroush Shahi, Stefany Cruz, Lingfeng Li, Sougata Sen, Mahdi Pedram, Christopher Romano, Josiah Hester, Aggelos K Katsaggelos, and Nabil Alshurafa. 2023. Smokemon: unobtrusive extraction of smoking topography using wearable energy-efficient thermal. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 6, 4 (20...

    2023

  4. [5]

    Lisa Anthony and Jacob O Wobbrock. 2010. A Lightweight Multistroke Recognizer for User Interface Prototypes. Proceedings of Graphics Interface 2010 2010 (2010), 8

    2010

  5. [6]

    Daniel Avrahami, Scott E Hudson, Thomas P Moran, and Brian D Williams. 2001. Guided Gesture Support in the Paper PDA.Proceedings of the 14th annual ACM symposium on User interface software and technology (2001), 2

    2001

  6. [7]

    Aaron Bangor, Philip T Kortum, and James T Miller. 2008. An empirical evaluation of the system usability scale. Intl. Journal of Human–Computer Interaction 24, 6 (2008), 574–594

    2008

  7. [8]

    Sarnab Bhattacharya, Rebecca Adaimi, and Edison Thomaz. 2022. Leveraging sound and wrist motion to detect activities of daily living with commodity smartwatches. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2 (2022), 1–28

    2022

  8. [9]

    Armanda Byberi, Maryam Ravan, and Reza K Amineh. 2023. GloveSense: A hand gesture recognition system based on inductive sensing. IEEE Sensors Journal 23, 9 (2023), 9210–9219

    2023

Show all 125 references
  1. [10]

    Altaf Hussain Chalkoo, Nusrat Nazir Makroo, and Gowhar Yaqub Peerzada. 2016. Exfoliative cheilitis. Indian J Dent Adv 8, 1 (2016), 56–9

    2016

  2. [11]

    Félix Chamberland, Étienne Buteau, Simon Tam, Evan Campbell, Ali Mortazavi, Erik Scheme, Paul Fortier, Mounir Boukadoum, Alexandre Campeau-Lecours, and Benoit Gosselin. 2023. Novel wearable HD-EMG sensor with shift-robust gesture recognition using deep learning. IEEE Transacti...

    2023

  3. [12]

    Chen Chen, Roozbeh Jafari, and Nasser Kehtarnavaz. 2015. UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In 2015 IEEE International conference on image processing (ICIP) . IEEE, 168–172

    2015

  4. [13]

    Ke-Yu Chen, Kent Lyons, Sean White, and Shwetak Patel. 2013. uTrack: 3D input using two magnetic sensors. In Proceedings of the 26th annual ACM symposium on User interface software and technology . ACM, St. Andrews Scotland, United Kingdom, 237–244. https://doi.org/10.1145/250...

    2013

  5. [14]

    Patel, and Sean Keller

    Ke-Yu Chen, Shwetak N. Patel, and Sean Keller. 2016. Finexus: Tracking Precise Motions of Multiple Fingertips Using Magnetic Sensing. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems . ACM, San Jose California USA, 1504–1514. https://doi.org/10.1...

    2016

  6. [15]

    Woohyeok Choi, Sangkeun Park, Duyeon Kim, Youn-kyung Lim, and Uichin Lee. 2019. Multi-stage receptivity model for mobile just-in-time health intervention. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 3, 2 (2019), 1–26

    2019

  7. [16]

    Woohyeok Choi, Sangkeun Park, Duyeon Kim, Youn-kyung Lim, and Uichin Lee. 2019. Multi-Stage Receptivity Model for Mobile Just-In-Time Health Intervention. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 2 (June 2019), 1–26. https://doi.or...

    2019 doi

  8. [17]

    Fernando De la Torre, Jessica Hodgins, Adam Bargteil, Xavier Martin, Justin Macey, Alex Collado, and Pep Beltran. 2009. Guide to the carnegie mellon university multimodal activity (cmu-mmac) database. (2009)

    2009

  9. [18]

    Joseph DelPreto, Josie Hughes, Matteo D’Aria, Marco De Fazio, and Daniela Rus. 2022. A wearable smart glove and its application of pose and gesture detection to sign language classification. IEEE Robotics and Automation Letters 7, 4 (2022), 10589–10596

    2022

  10. [20]

    Aiden Doherty, Dan Jackson, Nils Hammerla, Thomas Plötz, Patrick Olivier, Malcolm H Granat, Tom White, Vincent T Van Hees, Michael I Trenell, Christoper G Owen, et al. 2017. Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank ...

    2017

  11. [21]

    Tanja Döring, Dagmar Kern, Paul Marshall, Max Pfeiffer, Johannes Schöning, Volker Gruhn, and Albrecht Schmidt. 2011. Gestural interaction on the steering wheel: reducing the visual demand. (2011), 10

    2011

  12. [22]

    Marcus Georgi, Christoph Amma, and Tanja Schultz. 2015. Recognizing Hand and Finger Gestures with IMU based Motion and EMG based Muscle Activity Sensing:. InProceedings of the International Conference on Bio-inspired Systems and Signal Processing . SCITEPRESS - Science and and...

    2015 doi

  13. [24]

    Sophie A Greenberg, Bethanee J Schlosser, and Ginat W Mirowski. 2017. Diseases of the lips. Clinics in Dermatology 35, 5 (2017), e1–e14

    2017

  14. [25]

    Luke Haliburton, Saba Kheirinejad, Albrecht Schmidt, and Sven Mayer. 2023. Exploring Smart Standing Desks to Foster a Healthier Workplace. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, 2 (2023), 1–22

    2023

  15. [26]

    Yunjo Han, Hyemin Lee, Kobiljon E Toshnazarov, Youngtae Noh, and Uichin Lee. 2022. StressBal: Personalized Just-in-time Stress Intervention with Wearable and Phone Sensing. In Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Comput...

    2022

  16. [27]

    Chris Harrison, Desney Tan, and Dan Morris. 2010. Skinput: appropriating the body as an input surface. In Proceedings of the 28th international conference on Human factors in computing systems - CHI ’10 . ACM Press, Atlanta, Georgia, USA, 453. https: //doi.org/10.1145/1753326.1753394

    2010

  17. [28]

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778

    2016

  18. [29]

    Oscar Herrera-Alcántara, Ari Yair Barrera-Animas, Miguel González-Mendoza, and Félix Castro-Espinoza. 2019. Monitoring student activities with smartwatches: On the academic performance enhancement. Sensors 19, 7 (2019), 1605

    2019

  19. [30]

    Esther Howe, Jina Suh, Mehrab Bin Morshed, Daniel McDuff, Kael Rowan, Javier Hernandez, Marah Ihab Abdin, Gonzalo Ramos, Tracy Tran, and Mary P Czerwinski. 2022. Design of digital workplace stress-reduction intervention systems: Effects of intervention type and timing. In Proc...

    2022

  20. [31]

    Anne Hsu, Jing Yang, Yigit Han Yilmaz, Md Sanaul Haque, Cengiz Can, and Ann E Blandford. 2014. Persuasive technology for overcoming food cravings and improving snack choices. In Proceedings of the SIGCHI conference on human factors in computing systems . 3403–3412

    2014

  21. [32]

    Fang Hu, Peng He, Songlin Xu, Yin Li, and Cheng Zhang. 2020. FingerTrak: Continuous 3D Hand Pose Tracking by Deep Learning Hand Silhouettes Captured by Miniature Thermal Cameras on Wrist. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 2 ...

    2020 doi

  22. [33]

    Zhizhang Hu, Tong Yu, Yue Zhang, and Shijia Pan. 2020. Fine-grained activities recognition with coarse-grained labeled multi-modal data. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM ...

    2020

  23. [34]

    Zhizhang Hu, Tong Yu, Yue Zhang, and Shijia Pan. 2020. Fine-grained activities recognition with coarse-grained labeled multi-modal data. (2020), 644–649

    2020

  24. [35]

    2024.Self-Annotated Wearable Activity Data

    Alexander Hölzemann and Kristof Van Laerhoven. 2024.Self-Annotated Wearable Activity Data. https://doi.org/10.5281/zenodo.7654684

    2024 doi

  25. [36]

    Guillermo Iglesias, Edgar Talavera, Ángel González-Prieto, Alberto Mozo, and Sandra Gómez-Canaval. 2023. Data augmentation techniques in time series domain: a survey and taxonomy. Neural Computing and Applications 35, 14 (2023), 10123–10145. , Vol. 1, No. 1, Article . Publicat...

    2023

  26. [37]

    Yasha Iravantchi, Mayank Goel, and Chris Harrison. 2019. BeamBand: Hand Gesture Sensing with Ultrasonic Beamforming. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . ACM, Glasgow Scotland Uk, 1–10. https://doi.org/10. 1145/3290605.3300245

    2019

  27. [39]

    Pradier, Barbara Lam, Andrew C Ahn, Thomas H McCoy, Roy H Perlis, Finale Doshi-Velez, and Krzysztof Z Gajos

    Maia Jacobs, Jeffrey He, Melanie F. Pradier, Barbara Lam, Andrew C Ahn, Thomas H McCoy, Roy H Perlis, Finale Doshi-Velez, and Krzysztof Z Gajos. 2021. Designing AI for trust and collaboration in time-constrained medical decisions: a sociotechnical lens. In Proceedings of the 2...

    2021

  28. [40]

    Pyeong-Gook Jung, Gukchan Lim, Seonghyok Kim, and Kyoungchul Kong. 2015. A Wearable Gesture Recognition Device for Detecting Muscular Activities Based on Air-Pressure Sensors. IEEE Transactions on Industrial Informatics 11, 2 (April 2015), 485–494. https://doi.org/10.1109/TII....

    2015

  29. [41]

    Wolf Kienzle and Ken Hinckley. 2014. LightRing: always-available 2D input on any surface. In Proceedings of the 27th annual ACM symposium on User interface software and technology . ACM, Honolulu Hawaii USA, 157–160. https://doi.org/10.1145/2642918.2647376

    2014

  30. [43]

    Jaejeung Kim, Hayoung Jung, Minsam Ko, and Uichin Lee. 2019. Goalkeeper: Exploring interaction lockout mechanisms for regulating smartphone use. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 1 (2019), 1–29

    2019

  31. [44]

    Jaejeung Kim, Joonyoung Park, Hyunsoo Lee, Minsam Ko, and Uichin Lee. 2019. LocknType: Lockout task intervention for discouraging smartphone app use. In Proceedings of the 2019 CHI conference on human factors in computing systems . 1–12

    2019

  32. [45]

    Minwoo Kim, Jaechan Cho, Seongjoo Lee, and Yunho Jung. 2019. IMU Sensor-Based Hand Gesture Recognition for Human-Machine Interfaces. Sensors 19, 18 (Sept. 2019), 3827. https://doi.org/10.3390/s19183827

    2019 doi

  33. [46]

    Taewan Kim, Haesoo Kim, Ha Yeon Lee, Hwarang Goh, Shakhboz Abdigapporov, Mingon Jeong, Hyunsung Cho, Kyungsik Han, Youngtae Noh, Sung-Ju Lee, et al. 2022. Prediction for retrospection: Integrating algorithmic stress prediction into personal informatics systems for college stud...

    2022

  34. [47]

    Athanasios Kirmizis, Konstantinos Kyritsis, and Anastasios Delopoulos. 2021. Wrist-mounted IMU data towards the investigation of free-living smoking behavior - the Smoking Event Detection (SED) and Free-living Smoking Event Detection (SED-FL) datasets . https: //doi.org/10.528...

    2021 doi

  35. [48]

    Kevin Koch, Varun Mishra, Shu Liu, Thomas Berger, Elgar Fleisch, David Kotz, and Felix Wortmann. 2021. When do drivers interact with in-vehicle well-being interventions? An exploratory analysis of a longitudinal study on public roads. Proceedings of the ACM on interactive, mob...

    2021

  36. [49]

    Konstantinos Kyritsis, Christos Diou, and Anastasios Delopoulos. 2021. Wrist-mounted IMU data towards the investigation of in-meal human eating behavior - the Food Intake Cycle (FIC) dataset . https://doi.org/10.5281/zenodo.4421861

    2021 doi

  37. [50]

    Gierad Laput and Chris Harrison. 2019. Sensing Fine-Grained Hand Activity with Smartwatches. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19 (2019), 1–13. https://doi.org/10.1145/3290605.3300568

    2019

  38. [51]

    Gierad Laput, Robert Xiao, and Chris Harrison. 2016. ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology . ACM, Tokyo Japan, 321–333. https://doi.org/10.1145...

    2016

  39. [52]

    Chi-Jung Lee, Ruidong Zhang, Devansh Agarwal, Tianhong Catherine Yu, Vipin Gunda, Oliver Lopez, James Kim, Sicheng Yin, Boao Dong, Ke Li, et al. 2024. Echowrist: Continuous hand pose tracking and hand-object interaction recognition using low-power active acoustic sensing on a ...

    2024

  40. [53]

    Jongin Lee, Daeki Cho, Junhong Kim, Eunji Im, JinYeong Bak, Kyung Ho Lee, Kwan Hong Lee, and John Kim. 2017. Itchtector: a wearable-based mobile system for managing itching conditions. In Proceedings of the 2017 CHI conference on human factors in computing systems. 893–905

    2017

  41. [54]

    Zikang Leng, Amitrajit Bhattacharjee, Hrudhai Rajasekhar, Lizhe Zhang, Elizabeth Bruda, Hyeokhyen Kwon, and Thomas Plötz. 2024. Imugpt 2.0: Language-based cross modality transfer for sensor-based human activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearab...

    2024

  42. [55]

    Jiyang Li, Lin Huang, Siddharth Shah, Sean J Jones, Yincheng Jin, Dingran Wang, Adam Russell, Seokmin Choi, Yang Gao, Junsong Yuan, et al. 2023. Signring: Continuous american sign language recognition using imu rings and virtual imu data. Proceedings of the ACM on Interactive,...

    2023

  43. [56]

    Zisu Li, Chen Liang, Yuntao Wang, Yue Qin, Chun Yu, Yukang Yan, Mingming Fan, and Yuanchun Shi. 2023. Enabling voice- accompanying hand-to-face gesture recognition with cross-device sensing. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–17....

    2023

  44. [57]

    Zhuoyang Li, Minhui Liang, Ray Lc, and Yuhan Luo. 2024. StayFocused: Examining the Effects of Reflective Prompts and Chatbot Support on Compulsive Smartphone Use. In Proceedings of the CHI Conference on Human Factors in Computing Systems . 1–19

    2024

  45. [58]

    Peng Liao, Kristjan Greenewald, Predrag Klasnja, and Susan Murphy. 2020. Personalized heartsteps: A reinforcement learning algorithm for optimizing physical activity. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (2020), 1–22

    2020

  46. [59]

    Jiayang Liu, Lin Zhong, Jehan Wickramasuriya, and Venu Vasudevan. 2009. uWave: Accelerometer-based personalized gesture recognition and its applications. Pervasive and Mobile Computing 5, 6 (Dec. 2009), 657–675. https://doi.org/10.1016/j.pmcj.2009.07.007

    2009 doi

  47. [60]

    Yihua Lou, Wenjun Wu, Radu-Daniel Vatavu, and Wei-Tek Tsai. 2017. Personalized gesture interactions for cyber-physical smart-home environments. Science China Information Sciences 60, 7 (July 2017), 072104. https://doi.org/10.1007/s11432-015-1014-7

    2017 doi

  48. [61]

    Tao Lu, Hongxiao Zheng, Tianying Zhang, Xuhai “Orson” Xu, and Anhong Guo. 2024. InteractOut: Leveraging Interaction Proxies as Input Manipulation Strategies for Reducing Smartphone Overuse. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1–19

    2024

  49. [62]

    Yuhan Luo, Bongshin Lee, Donghee Yvette Wohn, Amanda L Rebar, David E Conroy, and Eun Kyoung Choe. 2018. Time for break: Understanding information workers’ sedentary behavior through a break prompting system. In Proceedings of the 2018 CHI Conference on Human Factors in Comput...

    2018

  50. [63]

    Adria Mallol-Ragolta, Anastasia Semertzidou, Maria Pateraki, and Björn Schuller. 2022. harAGE Corpus. https://doi.org/10.5281/zenodo. 6517688

    2022 doi

  51. [64]

    Miguel Matey-Sanz, Sven Casteleyn, and Carlos Granell. 2023. Dataset of inertial measurements of smartphones and smartwatches for human activity recognition. Data in Brief 51 (2023), 109809

    2023

  52. [65]

    McKenna and Kenny Morrison

    Stephen J. McKenna and Kenny Morrison. 2004. A comparison of skin history and trajectory-based representation schemes for the recognition of user-specified gestures. Pattern Recognition 37, 5 (May 2004), 999–1009. https://doi.org/10.1016/j.patcog.2003.09.007

    2004 doi

  53. [66]

    Jorge Mejia, David Meng, and Arun Sebastian. 2023. Enhancing understanding of real-world listening experiences: Insights from eco- logical momentary assessments with assistive listening technologies. The Journal of the Acoustical Society of America 154, 4_supplement (2023), A119–A119

    2023

  54. [67]

    Long Meng, Xinyu Jiang, Xiangyu Liu, Jiahao Fan, Haoran Ren, Yao Guo, Haikang Diao, Zihao Wang, Chen Chen, Chenyun Dai, et al

  55. [68]

    Kathryn R Middleton, Stephen D Anton, and Michal G Perri. 2013. Long-term adherence to health behavior change. American journal of lifestyle medicine 7, 6 (2013), 395–404

    2013

  56. [69]

    Thomas Mills, Rosie Shannon, Jane O’Hara, Rebecca Lawton, and Laura Sheard. 2022. Development of a ‘real-world’logic model through testing the feasibility of a complex healthcare intervention: the challenge of reconciling scalability and context-sensitivity. Evaluation 28, 1 (...

    2022

  57. [70]

    Varun Mishra, Florian Künzler, Jan-Niklas Kramer, Elgar Fleisch, Tobias Kowatsch, and David Kotz. 2021. Detecting Receptivity for mHealth Interventions in the Natural Environment. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 2 (June 20...

    2021 doi

  58. [71]

    Thomas Munder, Fabian Wilmers, Rainer Leonhart, Hans Wolfgang Linster, and Jürgen Barth. 2010. Working Alliance Inventory-Short Revised (WAI-SR): psychometric properties in outpatients and inpatients. Clinical Psychology & Psychotherapy: An International Journal of Theory & Pr...

    2010

  59. [72]

    Nacenta, Yemliha Kamber, Yizhou Qiang, and Per Ola Kristensson

    Miguel A. Nacenta, Yemliha Kamber, Yizhou Qiang, and Per Ola Kristensson. 2013. Memorability of pre-designed and user-defined gesture sets. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . ACM, Paris France, 1099–1108. https: //doi.org/10.1145/24...

    2013

  60. [73]

    Philyaw-Kotov, Predrag Klasnja, Erin E

    Inbal Nahum-Shani, Mashfiqui Rabbi, Jamie Yap, Meredith L. Philyaw-Kotov, Predrag Klasnja, Erin E. Bonar, Rebecca M. Cunningham, Susan A. Murphy, and Maureen A. Walton. 2021. Translating Strategies for Promoting Engagement in Mobile Health: A Proof-of- Concept Micro-Randomized...

    2021 doi

  61. [74]

    Inbal Nahum-Shani, Shawna N Smith, Bonnie J Spring, Linda M Collins, Katie Witkiewitz, Ambuj Tewari, and Susan A Murphy. 2018. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Annals of Beh...

    2018

  62. [75]

    Rajalakshmi Nandakumar, Vikram Iyer, Desney Tan, and Shyamnath Gollakota. 2016. FingerIO: Using Active Sonar for Fine-Grained Finger Tracking. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems . ACM, San Jose California USA, 1515–1525. https://doi...

    2016

  63. [76]

    Shrikanth Narayanan and Panayiotis G Georgiou. 2013. Behavioral signal processing: Deriving human behavioral informatics from speech and language. Proc. IEEE 101, 5 (2013), 1203–1233

    2013

  64. [77]

    Hong-Quan Nguyen, Trung-Hieu Le, Trung-Kien Tran, Hoang-Nhat Tran, Thanh-Hai Tran, Thi-Lan Le, Hai Vu, Cuong Pham, Thanh Phuong Nguyen, and Huu Thanh Nguyen. 2023. Hand Gesture Recognition From Wrist-Worn Camera for Human–Machine Interaction. IEEE Access 11 (2023), 53262–53274...

    2023

  65. [78]

    Ferda Ofli, Rizwan Chaudhry, Gregorij Kurillo, René Vidal, and Ruzena Bajcsy. 2013. Berkeley mhad: A comprehensive multimodal human action database. In 2013 IEEE workshop on applications of computer vision (W ACV). IEEE, 53–60

    2013

  66. [79]

    Adiba Orzikulova, Han Xiao, Zhipeng Li, Yukang Yan, Yuntao Wang, Yuanchun Shi, Marzyeh Ghassemi, Sung-Ju Lee, Anind K Dey, and Xuhai Xu. 2024. Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention. In Proceedings of the CHI Conference on Human F...

    2024

  67. [80]

    Atsushi Oshio. 2018. Who shake their legs and bite their nails? Self-reported repetitive behaviors and big five personality traits. Psychological Studies 63, 4 (2018), 384–390

    2018

  68. [81]

    Tom Ouyang and Yang Li. 2012. Bootstrapping personal gesture shortcuts with the wisdom of the crowd and handwriting recognition. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . ACM, Austin Texas USA, 2895–2904. https: //doi.org/10.1145/2207676.2208695

    2012

  69. [82]

    Farshid Salemi Parizi, Eric Whitmire, and Shwetak Patel. 2019. AuraRing: Precise Electromagnetic Finger Tracking. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 4 (Dec. 2019), 1–28. https://doi.org/10.1145/3369831

    2019 doi

  70. [83]

    Martin Pielot, Bruno Cardoso, Kleomenis Katevas, Joan Serrà, Aleksandar Matic, and Nuria Oliver. 2017. Beyond interruptibility: Predicting opportune moments to engage mobile phone users. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (...

    2017

  71. [84]

    Lindsay Prior. 2014. Content analysis. The Oxford handbook of qualitative research (2014), 359–379

    2014

  72. [85]

    Jing Qi, Li Ma, Zhenchao Cui, and Yushu Yu. 2024. Computer vision-based hand gesture recognition for human-robot interaction: a review. Complex & Intelligent Systems 10, 1 (2024), 1581–1606

    2024

  73. [86]

    Mashfiqui Rabbi, Min Hane Aung, Mi Zhang, and Tanzeem Choudhury. 2015. MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. 707–718

    2015

  74. [87]

    Jason Raether, Ehsanul Haque Nirjhar, and Theodora Chaspari. 2022. Evaluating Just-In-Time Vibrotactile Feedback for Communication Anxiety. In Proceedings of the 2022 International Conference on Multimodal Interaction . 117–127

    2022

  75. [88]

    Farokh Atashzar, and Arash Mohammadi

    Elahe Rahimian, Soheil Zabihi, Amir Asif, S. Farokh Atashzar, and Arash Mohammadi. 2021. Few-Shot Learning for Decoding Surface Electromyography for Hand Gesture Recognition. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP...

    2021

  76. [89]

    Elahe Rahimian, Soheil Zabihi, Amir Asif, Dario Farina, Seyed Farokh Atashzar, and Arash Mohammadi. 2021. FS-HGR: Few-shot learning for hand gesture recognition via electromyography. IEEE transactions on neural systems and rehabilitation engineering 29 (2021), 1004–1015

    2021

  77. [90]

    Daniel Roggen, Alberto Calatroni, Long-Van Nguyen-Dinh, Ricardo Chavarriaga, and Hesam Sagha. 2010. OPPORTUNITY Activity Recognition. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5M027

    2010 doi

  78. [91]

    Camilo Rojas, Niels Poulsen, Mileva Van Tuyl, Daniel Vargas, Zipporah Cohen, Joe Paradiso, Pattie Maes, Kevin Esvelt, and Fadel Adib. 2021. A scalable solution for signaling face touches to reduce the spread of surface-based pathogens. Proceedings of the ACM on Interactive, Mo...

    2021

  79. [92]

    Aaqib Saeed, Tanir Ozcelebi, and Johan Lukkien. 2019. Multi-task self-supervised learning for human activity detection. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 2 (2019), 1–30

    2019

  80. [93]

    Tan, Dan Morris, and Ravin Balakrishnan

    T Scott Saponas, Desney S. Tan, Dan Morris, and Ravin Balakrishnan. 2008. Demonstrating the feasibility of using forearm electromyo- graphy for muscle-computer interfaces. In Proceeding of the twenty-sixth annual CHI conference on Human factors in computing systems - CHI ’08. ...

    2008

  81. [94]

    Scott Saponas, Desney S

    T. Scott Saponas, Desney S. Tan, Dan Morris, Ravin Balakrishnan, Jim Turner, and James A. Landay. 2009. Enabling always-available input with muscle-computer interfaces. In Proceedings of the 22nd annual ACM symposium on User interface software and technology - UIST ’09. ACM Pr...

    2009

  82. [95]

    Hillol Sarker, Moushumi Sharmin, Amin Ahsan Ali, Md Mahbubur Rahman, Rummana Bari, Syed Monowar Hossain, and Santosh Kumar. 2014. Assessing the availability of users to engage in just-in-time intervention in the natural environment. In Proceedings of the 2014 ACM international...

    2014

  83. [96]

    Adwait Sharma, Christina Salchow-Hömmen, Vimal Suresh Mollyn, Aditya Shekhar Nittala, Michael A Hedderich, Marion Koelle, Thomas Seel, and Jürgen Steimle. 2023. SparseIMU: Computational design of sparse IMU layouts for sensing fine-grained finger microgestures. ACM Transaction...

    2023

  84. [97]

    Xiyuan Shen, Chun Yu, Xutong Wang, Chen Liang, Haozhan Chen, and Yuanchun Shi. 2024. MouseRing: Always-available Touchpad Interaction with IMU Rings. In Proceedings of the CHI Conference on Human Factors in Computing Systems . 1–19

    2024

  85. [98]

    Camille E Short, Ann DeSmet, Catherine Woods, Susan L Williams, Carol Maher, Anouk Middelweerd, Andre Matthias Müller, Petra A Wark, Corneel Vandelanotte, Louise Poppe, et al. 2018. Measuring engagement in eHealth and mHealth behavior change interventions: viewpoint of methodo...

    2018

  86. [99]

    Yuying Si, Sujie Chen, Ming Li, Siying Li, Yisen Pei, and Xiaojun Guo. 2022. Flexible strain sensors for wearable hand gesture recognition: from devices to systems. Advanced Intelligent Systems 4, 2 (2022), 2100046. , Vol. 1, No. 1, Article . Publication date: February 2025. W...

    2022

  87. [100]

    Arthur Sluÿters, Sébastien Lambot, and Jean Vanderdonckt. 2022. Hand gesture recognition for an off-the-shelf radar by electromagnetic modeling and inversion. In Proceedings of the 27th International Conference on Intelligent User Interfaces . 506–522

    2022

  88. [101]

    Ivar Snorrason, Emily J Ricketts, Christopher A Flessner, Martin E Franklin, Dan J Stein, and Douglas W Woods. 2012. Skin picking disorder is associated with other body-focused repetitive behaviors: Findings from an internet study. Ann Clin Psychiatry 24, 4 (2012), 292–299

    2012

  89. [102]

    Dan J Stein, Christopher A Flessner, Martin Franklin, Nancy J Keuthen, Christine Lochner, and Douglas W Woods. 2008. Is trichotil- lomania a stereotypic movement disorder? An analysis of body-focused repetitive behaviors in people with hair-pulling. Annals of Clinical Psychiat...

    2008

  90. [103]

    Dan J Stein, Dana JH Niehaus, Soraya Seedat, and Robin A Emsley. 1998. Phenomenology of stereotypic movement disorder. , 307–312 pages

    1998

  91. [104]

    Kenneth Stewart, Garrick Orchard, Sumit Bam Shrestha, and Emre Neftci. 2020. Online Few-Shot Gesture Learning on a Neuromorphic Processor. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 10, 4 (Dec. 2020), 512–521. https://doi.org/10.1109/ JETCAS.2020.3032058

    2020

  92. [105]

    Paul Strohmeier, Roel Vertegaal, and Audrey Girouard. 2012. With a flick of the wrist: stretch sensors as lightweight input for mobile devices. In Proceedings of the Sixth International Conference on Tangible, Embedded and Embodied Interaction . ACM, Kingston Ontario Canada, 3...

    2012

  93. [106]

    Juho Sun, Sangkeun Park, Gyuwon Jung, Yong Jeong, Uichin Lee, Kyong-Mee Chung, Changseok Lee, Heewon Kim, Suhyon Ahn, Ahsan Khandoker, et al. 2020. BeActive: Encouraging physical activities with just-in-time health intervention and micro financial incentives. In Proceedings of...

    2020

  94. [107]

    Siddharth Swaroop, Zana Buçinca, Krzysztof Z Gajos, and Finale Doshi-Velez. 2024. Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure. In Proceedings of the 29th International Conference on Intelligent User Interfaces . 138–154

    2024

  95. [108]

    Ellen J Teng, Douglas W Woods, Michael P Twohig, and Brook A Marcks. 2002. Body-focused repetitive behavior problems: Prevalence in a nonreferred population and differences in perceived somatic activity. Behavior Modification 26, 3 (2002), 340–360

    2002

  96. [109]

    Georgina SA Trapp, Siobhan Hickling, Hayley E Christian, Fiona Bull, Anna F Timperio, Bryan Boruff, Damber Shrestha, and Billie Giles-Corti. 2015. Individual, social, and environmental correlates of healthy and unhealthy eating. Health Education & Behavior 42, 6 (2015), 759–768

    2015

  97. [110]

    Khai Truong, Julie Kientz, Nilanjan Banerjee, AJ Brush, and Ratul Mahajan. 2015. Deployment Study Length: How Long Should a System Be Evaluated in the Wild? GetMobile: Mobile Computing and Communications 19, 2 (2015), 18–21

    2015

  98. [111]

    Terry T Um, Franz MJ Pfister, Daniel Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, and Dana Kulić. 2017. Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks. In Proceedings of the 19th ACM interna...

    2017

  99. [112]

    Yonatan Vaizman, Katherine Ellis, and Gert Lanckriet. 2017. Recognizing detailed human context in the wild from smartphones and smartwatches. IEEE pervasive computing 16, 4 (2017), 62–74

    2017

  100. [113]

    Bas Verplanken and Sheina Orbell. 2003. Reflections on past behavior: a self-report index of habit strength 1. Journal of applied social psychology 33, 6 (2003), 1313–1330

    2003

  101. [114]

    Yanxia Wei, Pinpin Zheng, Hui Deng, Xihui Wang, Xiaomei Li, and Hua Fu. 2020. Design features for improving mobile health intervention user engagement: systematic review and thematic analysis. Journal of medical Internet research 22, 12 (2020), e21687

    2020

  102. [115]

    Hongyi Wen, Julian Ramos Rojas, and Anind K. Dey. 2016. Serendipity: Finger Gesture Recognition using an Off-the-Shelf Smartwatch. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems . ACM, San Jose California USA, 3847–3851. https://doi.org/10.1145...

    2016

  103. [116]

    Di Wu, Fan Zhu, and Ling Shao. 2012. One shot learning gesture recognition from RGBD images. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2012), 7–12. https://doi.org/10.1109/CVPRW.2012.6239179

    2012

  104. [117]

    Erwin Wu, Ye Yuan, Hui-Shyong Yeo, Aaron Quigley, Hideki Koike, and Kris M. Kitani. 2020. Back-Hand-Pose: 3D Hand Pose Estimation for a Wrist-worn Camera via Dorsum Deformation Network. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology. ...

    2020

  105. [118]

    Ruolan Wu, Chun Yu, Xiaole Pan, Yujia Liu, Ningning Zhang, Yue Fu, Yuhan Wang, Zhi Zheng, Li Chen, Qiaolei Jiang, et al. 2024. MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention. In Proceedings of the CHI Conference on H...

    2024

  106. [119]

    Kang Xia, Wenzhong Li, Shiwei Gan, and Sanglu Lu. 2024. TS2ACT: Few-Shot Human Activity Sensing with Cross-Modal Co-Learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, 4 (2024), 1–22

    2024

  107. [120]

    Pathak, and Prasant Mohapatra

    Chao Xu, Parth H. Pathak, and Prasant Mohapatra. 2015. Finger-writing with Smartwatch: A Case for Finger and Hand Gesture Recognition using Smartwatch. In Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications . ACM, Santa Fe New Mexico USA...

    2015

  108. [121]

    Xuhai Xu, Jun Gong, Carolina Brum, Lilian Liang, Bongsoo Suh, Shivam Kumar Gupta, Yash Agarwal, Laurence Lindsey, Runchang Kang, Behrooz Shahsavari, et al. 2022. Enabling hand gesture customization on wrist-worn devices. In Proceedings of the 2022 CHI , Vol. 1, No. 1, Article ...

    2022

  109. [122]

    Xuhai Xu, Xin Liu, Han Zhang, Weichen Wang, Subigya Nepal, Yasaman Sefidgar, Woosuk Seo, Kevin S Kuehn, Jeremy F Huckins, Margaret E Morris, et al. 2023. GLOBEM: cross-dataset generalization of longitudinal human behavior modeling. Proceedings of the ACM on Interactive, Mobile...

    2023

  110. [123]

    Xuhai Xu, Tianyuan Zou, Han Xiao, Yanzhang Li, Ruolin Wang, Tianyi Yuan, Yuntao Wang, Yuanchun Shi, Jennifer Mankoff, and Anind K Dey. 2022. TypeOut: leveraging just-in-time self-affirmation for smartphone overuse reduction. In Proceedings of the 2022 CHI Conference on Human F...

    2022

  111. [124]

    Xing-Dong Yang, Tovi Grossman, Daniel Wigdor, and George Fitzmaurice. 2012. Magic finger: always-available input through finger instrumentation. In Proceedings of the 25th annual ACM symposium on User interface software and technology - UIST ’12 . ACM Press, Cambridge, Massach...

    2012

  112. [125]

    Hui-Shyong Yeo, Erwin Wu, Juyoung Lee, Aaron Quigley, and Hideki Koike. 2019. Opisthenar: Hand Poses and Finger Tapping Recognition by Observing Back of Hand Using Embedded Wrist Camera. In Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology....

    2019

  113. [126]

    Abdullah Yasir Yilmaz, Evzen Ruzicka, and Joseph Jankovic. 2024. Leg stereotypy syndrome: phenomenological and quantitative analysis. Journal of Neurology (2024), 1–6

    2024

  114. [127]

    Hang Yuan, Shing Chan, Andrew P Creagh, Catherine Tong, Aidan Acquah, David A Clifton, and Aiden Doherty. 2024. Self-supervised learning for human activity recognition using 700,000 person-days of wearable data. NPJ digital medicine 7, 1 (2024), 91

    2024

  115. [128]

    Yiran Zhao, Yujie Tao, Grace Le, Rui Maki, Alexander Adams, Pedro Lopes, and Tanzeem Choudhury. 2023. Affective Touch as Immediate and Passive Wearable Intervention. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 4 (2023), 1–23

    2023

  116. [129]

    Yongpan Zou, Yunshu Wang, Haozhi Dong, Yaqing Wang, Yanbo He, and Kaishun Wu. 2024. PreGesNet: Few-Shot Acoustic Gesture Recognition Based on Task-Adaptive Pretrained Networks. IEEE Transactions on Mobile Computing (2024). , Vol. 1, No. 1, Article . Publication date: February ...

    2024

  117. [2022]

    IEEE Transactions on Instrumentation and Measurement 71 (2022), 1–16

    User-tailored hand gesture recognition system for wearable prosthesis and armband based on surface electromyogram. IEEE Transactions on Instrumentation and Measurement 71 (2022), 1–16

    2022

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.